3D Shape Constraint for Facial Feature Localization Using Probabilistic-like Output

This paper presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian Mixture Model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with Active Shape Model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a co...

• Type: Conference
• Year: 2004
• Where: FGR
• Authors: Longbin Chen, Lei Zhang, HongJiang Zhang, Mohamed Abdel-Mottaleb
• Keywords: Biometrics, Candidate Feature Points, Facial Feature Points, Feature Points, FGR 2004